Gaussian Mixture Compression
- Forschungsthema/Bereich
- Artificial Intelligence
- Typ der Abschlussarbeit
- Bachelor / Master
- Startzeitpunkt
- -
- Bewerbungsschluss
- 30.06.2028
- Dauer der Arbeit
- 4 months(BSc) - 6 months(MSc)
Beschreibung
Model compression for Gaussian mixture is compelling for several reasons. First, expectation maximization is non convex, often requiring multiple random restarts; compressing a well converged model preserves its hard won optimum and avoids repeated runs. Second, compression without retraining is a major advantage, delivering smaller footprints and faster inference while keeping the learned distribution intact. Third, maintaining multiple storage and compute tiers of the same model—full, medium, and ultra-light—mirrors the ChatGPT-4 and 4-mini pattern: a unified capability surface scaled for latency and cost. This process enables adaptive deployment, edge compatibility, and efficient A/B testing without duplicating training pipelines and simplifies fleet managementVoraussetzung
- Voraussetzungen an Studierende
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- There are no hard constraints but the more programming and math you know the more you can have fun while doing the project.
- Studiengangsbereiche
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- Ingenieurwissenschaften
Informatik
- Ingenieurwissenschaften
Betreuung
- Titel, Vorname, Name
- Ali Darijani
- Organisationseinheit
- Computer Science(IAR/IES)
- E-Mail Adresse
- ali.darijani@iosb.fraunhofer.de
- Link zur eigenen Homepage/Personenseite
- Website
Bewerbung per E-Mail
- Bewerbungsunterlagen
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E-Mail Adresse für die Bewerbung
Senden Sie die oben genannten Bewerbungsunterlagen bitte per Mail an ali.darijani@iosb.fraunhofer.de
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